Análise das Séries Com Dados IMDB

Foram escolhidas três séries para esta análise, Mad Men, Sherlock e The Killing.

#Pensar se a média é realmente representativa
series_a_serem_analisadas = read_csv(here("data/series_from_imdb.csv"),
                                     progress = FALSE) %>%
                            filter(series_name %in% c("Mad Men", "Sherlock", "The Killing"))
Parsed with column specification:
cols(
  series_name = col_character(),
  episode = col_character(),
  series_ep = col_integer(),
  season = col_integer(),
  season_ep = col_integer(),
  url = col_character(),
  user_rating = col_double(),
  user_votes = col_double(),
  r1 = col_double(),
  r2 = col_double(),
  r3 = col_double(),
  r4 = col_double(),
  r5 = col_double(),
  r6 = col_double(),
  r7 = col_double(),
  r8 = col_double(),
  r9 = col_double(),
  r10 = col_double()
)
medias_imd_por_serie = group_by(series_a_serem_analisadas, series_name) %>% summarize(media_imdb = mean(user_rating))

A média das notas dadas pelos usuários a cada episódio das séries analisadas não são muito diferentes, sendo a maior nota, 8.8, de Sherlock. A nota de cada episódio é ponderada, levando em conta a quantidade de pessoas que votaram e a nota que cada uma deu.

medias_series = plot_ly(medias_imd_por_serie,
                        x = ~series_name,
                        y = ~media_imdb,
                        name = "Média IMDB Séries",
                        type = "bar",
                        color = ~series_name) %>%
                        layout(yaxis = list(title = "Média IMDB"),
                               xaxis = list(title = "Séries"),
                               barmode = "group")
medias_series

No entanto, podemos ver que a The Killing é a que possui uma distribuição de notas mais homogênea, enquanto que a dispersão das notas dos episódios de Mad Men e Sherlock são maiores. Além disso, podemos perceber que a mediana e a média de cada série estão próximas uma da outra. Confirmando que a média é representativa.

variacoes_notas = plot_ly(series_a_serem_analisadas,
                          x = ~series_name,
                          y = ~user_rating,
                          type = "box",
                          color = ~series_name)
variacoes_notas
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